A Simple Poverty Scorecard for the Philippines

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how it values successful inclusion or exclusion versus errors of undercoverage andleakage. It is healthy to go through a process of thinking explicitly and intentionallyabout how possible targeting outcomes are valued.A common choice of benefits and costs is “Total Accuracy” (IRIS Center, 2005;Grootaert and Braithwaite, 1998). With “Total Accuracy”, total net benefit is thenumber of households correctly included or correctly excluded:Total Accuracy = 1 x Households correctly included –0 x Households mistakenly undercovered –0 x Households mistakenly leaked +1 x Households correctly excluded.Figure 12 shows “Total Accuracy” for all cut-offs for the Philippines’ scorecard.For the national line in the validation sample, total net benefit is greatest (81.5) for acut-off of 30–34, with about four in five Filipino households correctly classified.“Total Accuracy” weighs successful inclusion of households below the line thesame as successful exclusion of households above the line. If a program valued inclusionmore (say, twice as much) than exclusion, it could reflect this by setting the benefit forinclusion to 2 and the benefit for exclusion to 1. Then the chosen cut-off wouldmaximize (2 x Households correctly included) + (1 x Households correctly excluded). 2828Figure 12 also reports “BPAC”, the Balanced Poverty Accuracy Criteria adopted byUSAID as its criterion for certifying poverty scorecards. IRIS Center (2005) says thatBPAC considers accuracy both in terms of the estimated poverty rate and in terms oftargeting inclusion. After normalizing by the number of people below the poverty line,the formula is:BPAC = (Inclusion – |Undercoverage – Leakage|) x [100 ÷ (Inclusion+Undercoverage)].43
As an alternative to assigning benefits and costs to targeting outcomes and thenchoosing a cut-off to maximize total net benefit, a program could set a cut-off toachieve a desired poverty rate among targeted households. The third column of Figure13 (“% targeted who are poor”) shows, for the Philippines’ scorecard applied to thevalidation sample, the expected poverty rate among households who score at or below agiven cut-off. For the example of the national line, targeting households who score 35–39 or less would target 34.5 percent of all Filipino households and produce a povertyrate among those targeted of 68.8 percent.Figure 13 also reports two other measures of targeting accuracy. The first is aversion of coverage (“% of poor who are targeted”). For the example of the national lineand a cut-off of 35–39, 75.0 percent of all poor households are covered.The final targeting measure in Figure 13 is the number of successfully targetedpoor households for each non-poor household mistakenly targeted (right-most column).For the national line and a cut-off of 35–39, covering 2.2 poor households means leakingto 1 non-poor household.Although inclusion (and therefore targeting accuracy) appears in the BPAC formula,BPAC is in fact maximized (for a given poverty line and a single-step scorecard) whenthe difference between the estimated poverty rate and its true value is minimized,regardless of inclusion. Thus, selecting a scorecard on the basis of BPAC is equivalentto selecting on the basis of the difference between the estimated poverty rate and itstrue value (what IRIS calls “PIE”). It would therefore be clearer to drop the BPACnomenclature and simply discuss directly the accuracy and precision of the estimatedpoverty rate.44
Page 1 and 2: A Simple Poverty Scorecard for the
Page 3 and 4: comparable across organizations nor
Page 5 and 6: Poverty scoring can also be used fo
Page 7 and 8: points or less for estimates of cha
Page 9 and 10: counted as if it had only one perso
Page 11 and 12: derived from the 2000 FIES by the N
Page 13 and 14: Given this, the USD1.25/day 2005 PP
Page 15 and 16: Reyes builds a scorecard using the
Page 17 and 18: that quickly show how estimated pov
Page 19 and 20: 3.3 Gwatkin et al.Gwatkin et al. (2
Page 21 and 22: includes income, the PCA index cann
Page 23 and 24: One of these one-indicator scorecar
Page 25 and 26: The scorecard in Figure 1 is ready
Page 27 and 28: If not determined by other factors,
Page 29 and 30: 6.1 Calibrating scores with poverty
Page 31 and 32: 6.2 Accuracy of estimates of househ
Page 33 and 34: The 90-percent confidence interval
Page 35 and 36: 7. Estimates of a group’s poverty
Page 37 and 38: To derive a sample-size formula (or
Page 39 and 40: 21.64n = 0. 55 ⋅⎛ ⎞⎜ ⎟
Page 41 and 42: 8. Estimates of changes in group po
Page 43 and 44: 2002 in the Philippines is the diff
Page 45 and 46: from 0.63 to 0.77 (Figure 10), sugg
Page 47 and 48: To illustrate the use of (8), suppo
Page 49: the four possible targeting outcome
Page 53 and 54: at a point in time is always less t
Page 55 and 56: ReferencesAdams, N.M.; and D.J. Han
Page 57 and 58: Hentschel, Jesko; Lanjouw, Jean Ols
Page 59 and 60: Onwujekwe, Obinna; Hanson, Kara; an
Page 61 and 62: Stillwell, William G.; Barron, F. H
Page 63 and 64: Figure 1: A simple poverty scorecar
Page 65 and 66: Figure 3: Poverty indicators by unc
Page 67 and 68: Figure 3 (cont.): Poverty indicator
Page 69 and 70: National Poverty Line Tables(and Ta
Page 71 and 72: Figure 5 (National poverty line): D
Page 73 and 74: Figure 7 (National poverty line): B
Page 75 and 76: Figure 9 (National poverty line): D
Page 77 and 78: Figure 11 (All poverty lines): Poss
Page 79 and 80: Figure 13 (National poverty line):
Page 81 and 82: Figure 4 (National food poverty lin
Page 83 and 84: Figure 7 (National food poverty lin
Page 85 and 86: Figure 12 (National food poverty li
Page 87 and 88: USAID “Extreme” Poverty Line Ta
Page 89 and 90: Figure 5 (USAID “Extreme” pover
Page 91 and 92: Figure 9 (USAID “Extreme” pover
Page 93 and 94: Figure 13 (USAID “Extreme” pove
Page 95 and 96: Figure 4 (USD1.25/day 2005 PPP line
Page 97 and 98: Figure 7 (USD1.25/day 2005 PPP line
Page 99 and 100: Figure 12 (USD1.25/day 2005 PPP lin
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USD2.50/Day 2005 PPP Poverty Line T
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Figure 5 (USD2.50/day 2005 PPP line
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Figure 13 (USD2.50/day 2005 PPP lin
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USD5.00/Day 2005 PPP Poverty Line T
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National Poverty Line Tablesfor 200
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Figure 9 (National line): Differenc
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Figure 13 (National line): For a gi
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Figure 7 (Food line): Bootstrapped
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Figure 12 (Food line): Households b
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USAID “Extreme” Poverty Line Ta
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Figure 9 (USAID “extreme” line)
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Figure 13 (USAID “extreme” line
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USD2.50/day 2005 PPP Poverty Line T
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USD5.00/day 2005 PPP Poverty Line T
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Poverty Lines and Poverty Rates,by
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Figure A2: All-urban Philippines an
Page 173 and 174:
Figure A4: Agusan del Norte, povert
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Figure A6: Aklan, poverty lines and
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Figure A8: Antique, poverty lines a
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Figure A10: Bataan, poverty lines a
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Figure A12: Batangas, poverty lines
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Figure A14: Bohol, poverty lines an
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Figure A16: Bulacan, poverty lines
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Figure A18: Camarines Norte, povert
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Figure A20: Camiguin, poverty lines
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Figure A22: Catanduanes, poverty li
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Figure A24: Cebu, poverty lines and
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Figure A26: Davao del Sur, poverty
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Figure A28: Eastern Samar, poverty
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Figure A30: Ilocos Norte, poverty l
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Figure A32: Iloilo, poverty lines a
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Figure A34: Kalinga, poverty lines
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Figure A36: Laguna, poverty lines a
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Figure A38: Lanao del Sur, poverty
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Figure A40: Maguindanao, poverty li
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Figure A42: Marinduque, poverty lin
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Figure A44: Misamis Occidental, pov
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Figure A46: Mountain Province, pove
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Figure A48: Negros Oriental, povert
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Figure A50: Northern Samar, poverty
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Figure A52: Nueva Vizcaya, poverty
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Figure A54: Oriental Mindoro, pover
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Figure A56: Pampanga, poverty lines
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Figure A58: Quezon, poverty lines a
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Figure A60: Rizal, poverty lines an
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Figure A62: Western Samar, poverty
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Figure A64: Sorsogon, poverty lines
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Figure A66: Southern Leyte, poverty
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Figure A68: Sulu, poverty lines and
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Figure A70: Surigao del Sur, povert
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Figure A72: Tawi-tawi, poverty line
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Figure A74: Zamboanga del Norte, po
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Figure A76: 2nd District, poverty l
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Figure A78: 4th District, poverty l
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Figure A80: Biliran, poverty lines
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Figure A82: Saranggani, poverty lin
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Figure A84: Compostela Valley, pove
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Figure A86: Shariff Kabunsuan, pove
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Figure A88: Isabela City, poverty l
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A Simple Poverty Scorecard for the Philippines

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